| Challenge: | a growing number of fake news sites are used for spreading fake news . a lack of a reliable data set is limiting the use of machine learning in fact-checking . |
| Approach: | They propose a web application that can detect the origin of a rumour by identifying its source . |
| Outcome: | The proposed application can detect fake news claims with better accuracy than humans . |
Similar Papers
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (D18-1)
Copied to clipboard
| Challenge: | Recent work on automated fact-checking does not consider external evidence, but requires rich lexicons. |
| Approach: | They propose a neural network model that aggregates external evidence and language . they also derive informative features for generating user-comprehensible explanations . |
| Outcome: | The proposed model aggregates signals from external evidence articles, language and trustworthiness of their sources without human intervention. |
Automatic Detection of Fake News (C18-1)
Copied to clipboard
| Challenge: | a growing number of fake news detection tools are needed to identify trustworthy news sources. |
| Approach: | They propose to use two novel datasets to automate the identification of fake news . they propose learning experiments to build accurate fake news detectors . |
| Outcome: | The proposed algorithms achieve accuracies of up to 76% and compare them with other tools . the proposed algorithms are based on satirical news sources and fact-checking websites . |
LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification (2021.findings-acl)
Copied to clipboard
| Challenge: | Automated fact-checking is time-consuming and cannot scale due to a lack of suitable training data. |
| Approach: | They propose to use a dataset to automatically check facts and a text classifier to infer the likelihood of the input being a piece of fake-news. |
| Outcome: | The proposed dataset VERITAS and LUX use linguistic analysis to infer the likelihood of the input being a piece of fake-news. |
Automatic Fake News Detection: Are Models Learning to Reason? (2021.acl-short)
Copied to clipboard
| Challenge: | Existing methods for fake news detection rely on reasoning . existing work has not explored the predictive power of isolated evidence . |
| Approach: | They investigate the relationship and importance of both claim and evidence in fact checking models. |
| Outcome: | The proposed model performs better on political fact checking datasets using both the claim and evidence. |
Annotation-Scheme Reconstruction for “Fake News” and Japanese Fake News Dataset (2022.lrec-1)
Copied to clipboard
| Challenge: | Contemporary research focuses on the factuality aspect of the news, but this aspect alone is insufficient to explain “fake news.” |
| Approach: | They propose to use Japanese fake news datasets to classify whether news content is false . they propose to do this by using existing fake news data to investigate fake news . |
| Outcome: | The proposed scheme will provide an in-depth understanding of fake news in Japan and other languages. |
Discovering Biased News Articles Leveraging Multiple Human Annotations (2020.lrec-1)
Copied to clipboard
| Challenge: | Political propaganda and one-sided views can be found in the news and can cause distrust in media. |
| Approach: | They propose to annotate politically biased news articles by an algorithm annotated by domain experts and crowd workers and to compare them to crowd workers. |
| Outcome: | The proposed method compares domain experts to crowd workers and shows that bias can be detected automatically. |
BREAKING! Presenting Fake News Corpus for Automated Fact Checking (P19-2)
Copied to clipboard
| Challenge: | a new study shows that fake news spreads faster than mainstream articles on the same topic . however, there is no dataset containing compelling fake and questionable news articles . |
| Approach: | They introduce manually verified corpus of compelling fake and questionable news articles on the USA politics . they plan to extend the corpus in the future and use it for automated fake news detection. |
| Outcome: | The proposed model is based on linguistic features and will be extended in the future . it will be used to improve the existing model and improve the tools in the field of fake news detection . |
Automatic and Manual Web Annotations in an Infrastructure to handle Fake News and other Online Media Phenomena (L18-1)
Copied to clipboard
| Challenge: | a growing number of people consume news online, but there are different types of "fake news" many online news outlets use the same journalistic principles that have been in use for newspapers for decades, especially factchecking. |
| Approach: | They propose a metadata scheme to enable users to handle "fake news" they also propose 'filter bubble' effect and abuse language . |
| Outcome: | The proposed metadata scheme enables standardisation of these phenomena in online media. |
A Multi-Label Dataset of French Fake News: Human and Machine Insights (2024.lrec-main)
Copied to clipboard
Benjamin Icard, François Maine, Morgane Casanova, Géraud Faye, Julien Chanson, Guillaume Gadek, Ghislain Atemezing, François Bancilhon, Paul Égré
| Challenge: | a corpus of documents selected from 17 sources of french press considered unreliable by experts is annotated using 11 labels by 8 annotators. |
| Approach: | They present a corpus of documents annotated using 11 labels by 8 annotators . they use a subjectivity analyzer VAGO to clarify the link between subjective and fake news labels . |
| Outcome: | The proposed dataset identifies features that humans consider as characteristic of fake news . it also clarifies the link between ascriptions of the label Subjective and ascribeds of Fake News . |
Explainable Tsetlin Machine Framework for Fake News Detection with Credibility Score Assessment (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing models for fake news classification are difficult to explain and quality-assure . however, they are black-box-based and lack a clear explanation of their decisions. |
| Approach: | They propose an interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM) they use conjunctive clauses to capture lexical and semantic properties of both true and fake news text and use clause ensembles to calculate the credibility of fake news. |
| Outcome: | The proposed framework outperforms baseline models on PolitiFact and GossipCop datasets in terms of accuracy and provides higher F1-score than BERT and XLNet, but lower accuracy. |